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R Programming for Data Science

Course Overview Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no …

R Programming for Data Science

R Programming for Data Science

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6 hours, 25 minutes

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Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow R Programming for Data Science will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course. 

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge. 

Entry Requirement

    • This course is available to all learners of all academic backgrounds.
    • Learners should be aged 16 or over to undertake the qualification.
    • Some basic understanding of the English language and numeracy.  

Certification

After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can, however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £29 or in PDF format at the cost of £19.

    • PDF certificate’s turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days

Why choose us?

      • Affordable, engaging & high-quality e-learning study materials;
      • Tutorial videos/materials from the industry-leading experts;
      • Study in a user-friendly, advanced online learning platform;
      • Efficient exam systems for the assessment and instant result;
      • The UK & internationally recognised accredited qualification;
      • Access to course content on mobile, tablet or desktop from anywhere, anytime;
      • The benefit of career advancement opportunities;
      • 24/7 student support via email.

Career path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry. 

Course Curriculum

Unit 01: Data Science Overview
Introduction to Data Science 00:01:00
Data Science: Career of the Future 00:04:00
What is Data Science? 00:02:00
Data Science as a Process 00:02:00
Data Science Toolbox 00:03:00
Data Science Process Explained 00:05:00
What’s Next? 00:01:00
Unit 02: R and RStudio
Engine and coding environment 00:03:00
Installing R and RStudio 00:04:00
RStudio: A quick tour 00:04:00
Unit 03: Introduction to Basics
Arithmetic with R 00:03:00
Variable assignment 00:04:00
Basic data types in R 00:03:00
Unit 04: Vectors
Creating a vector 00:05:00
Naming a vector 00:04:00
Vector selection 00:06:00
Selection by comparison 00:04:00
Unit 05: Matrices
What’s a Matrix? 00:02:00
Analyzing Matrices 00:03:00
Naming a Matrix 00:05:00
Adding columns and rows to a matrix 00:06:00
Selection of matrix elements 00:03:00
Arithmetic with matrices 00:07:00
Additional Materials 00:00:00
Unit 06: Factors
What’s a Factor? 00:02:00
Categorical Variables and Factor Levels 00:04:00
Summarizing a Factor 00:01:00
Ordered Factors 00:05:00
Unit 07: Data Frames
What’s a Data Frame? 00:03:00
Creating Data Frames 00:20:00
Selection of Data Frame elements 00:03:00
Conditional selection 00:03:00
Sorting a Data Frame 00:03:00
Additional Materials 00:00:00
Unit 08: Lists
Why would you need lists? 00:01:00
Creating a List 00:06:00
Selecting elements from a list 00:03:00
Adding more data to the list 00:02:00
Additional Materials 00:00:00
Unit 09: Relational Operators
Equality 00:03:00
Greater and Less Than 00:03:00
Compare Vectors 00:03:00
Compare Matrices 00:02:00
Additional Materials 00:00:00
Unit 10: Logical Operators
AND, OR, NOT Operators 00:04:00
Logical operators with vectors and matrices 00:04:00
Reverse the result: (!) 00:01:00
Relational and Logical Operators together 00:06:00
Additional Materials 00:00:00
Unit 11: Conditional Statements
The IF statement 00:04:00
IF…ELSE 00:03:00
The ELSEIF statement 00:05:00
Full Exercise 00:03:00
Additional Materials 00:00:00
Unit 12: Loops
Write a While loop 00:04:00
Looping with more conditions 00:04:00
Break: stop the While Loop 00:04:00
What’s a For loop? 00:02:00
Loop over a vector 00:02:00
Loop over a list 00:03:00
Loop over a matrix 00:04:00
For loop with conditionals 00:01:00
Using Next and Break with For loop 00:03:00
Additional Materials 00:00:00
Unit 13: Functions
What is a Function? 00:02:00
Arguments matching 00:03:00
Required and Optional Arguments 00:03:00
Nested functions 00:02:00
Writing own functions 00:03:00
Functions with no arguments 00:02:00
Defining default arguments in functions 00:04:00
Function scoping 00:02:00
Control flow in functions 00:03:00
Additional Materials 00:00:00
Unit 14: R Packages
Installing R Packages 00:01:00
Loading R Packages 00:04:00
Different ways to load a package 00:02:00
Additional Materials 00:00:00
Unit 15: The Apply Family - lapply
What is lapply and when is used? 00:04:00
Use lapply with user-defined functions 00:03:00
lapply and anonymous functions 00:01:00
Use lapply with additional arguments 00:04:00
Additional Materials 00:00:00
Unit 16: The apply Family – sapply & vapply
What is sapply? 00:02:00
How to use sapply 00:02:00
sapply with your own function 00:02:00
sapply with a function returning a vector 00:02:00
When can’t sapply simplify? 00:02:00
What is vapply and why is it used? 00:04:00
Additional Materials 00:00:00
Unit 17: Useful Functions
Mathematical functions 00:05:00
Data Utilities 00:08:00
Additional Materials 00:00:00
Unit 18: Regular Expressions
grepl & grep 00:04:00
Metacharacters 00:05:00
sub & gsub 00:02:00
More metacharacters 00:04:00
Additional Materials 00:00:00
Unit 19: Dates and Times
Today and Now 00:02:00
Create and format dates 00:06:00
Create and format times 00:03:00
Calculations with Dates 00:03:00
Calculations with Times 00:07:00
Additional Materials 00:00:00
Unit 20: Getting and Cleaning Data
Get and set current directory 00:04:00
Get data from the web 00:04:00
Loading flat files 00:03:00
Loading Excel files 00:05:00
Additional Materials 00:00:00
Unit 21: Plotting Data in R
Base plotting system 00:03:00
Base plots: Histograms 00:03:00
Base plots: Scatterplots 00:05:00
Base plots: Regression Line 00:03:00
Base plots: Boxplot 00:03:00
Unit 22: Data Manipulation with dplyr
Introduction to dplyr package 00:04:00
Using the pipe operator (%>%) 00:02:00
Columns component: select() 00:05:00
Columns component: rename() and rename_with() 00:02:00
Columns component: mutate() 00:02:00
Columns component: relocate() 00:02:00
Rows component: filter() 00:01:00
Rows component: slice() 00:04:00
Rows component: arrange() 00:01:00
Rows component: rowwise() 00:02:00
Grouping of rows: summarise() 00:03:00
Grouping of rows: across() 00:02:00
COVID-19 Analysis Task 00:08:00
Additional Materials 00:00:00
Assignment
Assignment – R Programming for Data Science 00:00:00

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